Online image repositories have extensive quantities of image content depicting a wide variety of objects in the images. Many of those depicted objects have appearance options based on multiple colors or patterns. Therefore, creation of “swatch” images that represent a particular color, pattern, or other appearance exemplar is necessary. But existing techniques for swatch creation are manual, expensive, and reliant on an individual to visually analyze the content of an image and select a representative region for the swatch creation.
For example, an image depicts an object that comes in a variety of colors or patterns. The depicted object may have different areas that display portions of the colors or pattern, such as a shirt that has a torso in one color and sleeves in a different color. Current techniques to select swatches representative of the different colors or patterns are manually intensive and may require a relatively long amount of time to select accurate swatches for a large set of images. For example, manual selection of swatches may require a person, such as a graphic designer, to spend several minutes reviewing an image, determining one or more representative areas of an object depicted in the image, and providing inputs to a user interface to indicate the swatches he or she is selecting as the representative areas. For a large group of images (e.g., hundreds or thousands of images), the amount of time, computing resources, or financial resources required to manually select the representative swatches may be prohibitively high.
In addition, manual techniques for selecting swatches reach inconsistent results when determining what constitutes a swatch that most accurately represents an appearance of a depicted object. For example, when selecting swatches for a large group of images, different people reviewing various images in the group may select swatches based on their personal interpretations of the images. Differences between the people reviewing the images may result in inconsistent swatches that represent the images poorly. Also, variations in size and content of a manually selected swatch can reduce the representativeness of the swatch. For example, selected swatches that represent a particular area of the image (e.g., a logo, or a small portion of a varied pattern) may not represent multiple diverse aspects of the depicted object compared to other areas of the image (e.g., a background), depending on the size and overall content of the image.
Therefore, existing solutions may involve disadvantages for the reasons such as (but not limited to) those described above.